Development of an AI-Based Segmentation Technique for Automatic Classification of Impacted Supernumerary Teeth: A Multicenter Retrospective CBCT Imaging Study

Status

Active, not recruiting

Phases

Phase 1

Study type

Observational

Source

ChiCTR

Registry ID

ChiCTR2500098765

Enrollment

Unknown

Registered

2025-03-13

Start date

2025-03-13

Completion date

Unknown

Last updated

2025-03-17

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Impacted supernumerary tooth

Interventions

Observation group:None

Eligibility

Sex/Gender

All

Age

5 Years to 30 Years

Inclusion criteria

Inclusion criteria: 1. Clear and complete CBCT imaging data. 2. CBCT data must have a slice thickness of 0.15–0.35 mm.

Exclusion criteria

Exclusion criteria: Low-quality CBCT data, such as those with metal artifacts, patient motion artifacts, or blurred CBCT images.

Design outcomes

Primary

Measure	Time frame
CBCT images;	—

Countries

China

Contacts

Public ContactWen Xiao

Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine

xiaowen@shsmu.edu.cn+86 180 1928 8275

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026